Article
Meteorology & Atmospheric Sciences
Haohao Sun, Lili Lei, Zhengyu Liu, Liang Ning, Zhe-Min Tan
Summary: An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. AOEnKF generates smaller posterior errors and requires much less computational cost compared to the online cycling EnKF (CEnKF). It has the advantages of having a more accurate prior ensemble mean and flow-dependent background error covariances compared to the commonly applied offline EnKF (OEnKF).
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Jeffrey S. Whitaker, Anna Shlyaeva, Stephen G. Penny
Summary: This study compares two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system: the hybrid-covariance approach and the hybrid-gain approach. The results show that the simpler and less expensive hybrid-gain approach can achieve similar performance if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Water Resources
Andrew Pensoneault, Witold F. Krajewski, Nicolas Velasquez, Xueyu Zhu, Ricardo Mantilla
Summary: This paper discusses the application of data assimilation techniques in hydrology, focusing on the potential of EnKF and its extensions in sequential state estimation and Bayesian inverse problems. The authors improve the streamflow in a virtual catchment using the EKI algorithm and demonstrate its favorable performance.
ADVANCES IN WATER RESOURCES
(2023)
Article
Mathematics, Applied
Wenxuan Xie, Yibao Li
Summary: In this study, a data assimilation framework is proposed for simulating microstructural evolutions in the phase field crystal model. The sequential data assimilation method based on the ensemble Kalman filter is used to integrate phase field simulation and experimental observational data, leading to improved simulation accuracy. Additionally, by coupling the framework with a second-order accurate unconditional energy stable scheme, stability restrictions are avoided.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Meteorology & Atmospheric Sciences
Lars Nerger
Summary: The study introduces a hybrid filter combining LETKF and NETF with the performance improved by adjusting the hybrid weight. Results show that a hybrid variant applying NETF followed by LETKF yields the best results in complex nonlinear models. Calculating the hybrid weight based on skewness, kurtosis, and effective sample size reduces estimation errors and enhances stability of the hybrid filter.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Cardiac & Cardiovascular Systems
Dario De Marinis, Dominik Obrist
Summary: The proposed data assimilation methodology aims to enhance the spatial and temporal resolution of voxel-based data obtained from biomedical imaging modalities, specifically focusing on turbulent blood flow assessment in large vessels. The methodology, utilizing a Stochastic Ensemble Kalman Filter approach, combines observed flow fields with numerical simulations to improve the accuracy of flow field predictions. Validation against canonical flows and application to a clinically relevant scenario demonstrate the potential of the method to enhance 4D flow MRI data for future use.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Meteorology & Atmospheric Sciences
S. G. Penny, T. A. Smith, T-C Chen, J. A. Platt, H-Y Lin, M. Goodliff, H. D. Abarbanel
Summary: This article introduces the integration of data assimilation (DA) with machine learning for entirely data-driven online state estimation. Recurrent neural networks (RNNs) are used as pretrained surrogate models to replace key components in numerical weather prediction (NWP) and can be initialized using DA methods to estimate the state of a system.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Environmental Sciences
Rajsekhar Kandala, Harrie-Jan Hendricks Franssen, Abhijit Chaudhuri, M. Sekhar
Summary: This study explores the value of using near-surface soil moisture and soil temperature measurements to estimate soil parameters and heat fluxes. The results show that assimilating soil temperature can improve the accuracy of soil moisture and heat flux estimation, especially for clayey soils. Assimilating both soil moisture and soil temperature further enhances the model's performance for different climate and soil conditions.
VADOSE ZONE JOURNAL
(2023)
Article
Meteorology & Atmospheric Sciences
Troy Arcomano, Istvan Szunyogh, Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Edward Ott
Summary: This paper describes the implementation of a combined hybrid-parallel prediction approach on a low-resolution atmospheric global circulation model. The hybrid model, which combines a physics-based numerical model with a machine learning component, produces more accurate forecasts for various atmospheric variables compared to the host model. Furthermore, the hybrid model exhibits smaller systematic errors and more realistic temporal variability in simulating the climate.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Engineering, Ocean
Shintaro Gomi, Tsutomu Takagi, Katsuya Suzuki, Rika Shiraki, Ichiya Ogino, Shigeru Asaumi
Summary: A control method for changing the geometry of a fishing net was proposed, utilizing data assimilation to estimate unknown parameters and achieve the intended net geometry. The automatic control system was validated through numerical simulation experiments, demonstrating the successful control of net geometry using the extended Kalman filter.
APPLIED OCEAN RESEARCH
(2021)
Article
Environmental Sciences
X. D. Lyu, Y. R. Fan
Summary: The study employs a multi-level factorial analysis approach to characterize the major impact factors on the performances of different data assimilation schemes, demonstrating that the impacts from stochastic perturbations vary for different schemes and some factors may be statistically insignificant. Results indicate that scenarios with extreme stochastic perturbations are more likely to result in good performance for all data assimilation schemes.
JOURNAL OF ENVIRONMENTAL INFORMATICS
(2021)
Article
Agronomy
Izael Martins Fattori Junior, Murilo dos Santos Vianna, Fabio Ricardo Marin
Summary: The ability to estimate sugarcane yield is crucial for improving planning, food, and energy security. This study evaluates the performance of a crop model coupled with data assimilation algorithms, as well as the impact of parameter calibration and timing of observations on yield predictions. The results show that using data assimilation methods improves yield predictions, and genotype-specific parameter calibration further enhances accuracy.
EUROPEAN JOURNAL OF AGRONOMY
(2022)
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Multidisciplinary Sciences
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson, Nancy Collins, Jeffrey L. Anderson, Jeff Steward, Mick Coady
Summary: An ensemble Kalman filter reanalysis data set with a global, 80 member ensemble spanning from 2011 to 2019 is archived, providing opportunities for robust statistical analysis and machine learning training.
SCIENTIFIC REPORTS
(2021)
Article
Environmental Sciences
G. Piazzi, G. Thirel, C. Perrin, O. Delaigue
Summary: Skillful streamflow forecasts are crucial for water-related applications, with a growing emphasis on improving initial condition estimates through data assimilation. This study assesses the sensitivity of DA-based IC estimation to various uncertainties and model updates over 232 watersheds in France. The comparison of two ensemble-based techniques shows that accurate routing store estimates benefit the DA-based IC estimation, with the EnKF outperforming the PF in forecasting meteorological uncertainty.
WATER RESOURCES RESEARCH
(2021)
Article
Water Resources
Judith Fournier, Antoine Thiboult, Daniel F. Nadeau, Nikki Vercauteren, Francois Anctil, Annie-Claude Parent, Ian B. Strachan, Alain Tremblay
Summary: This study utilized long-term eddy covariance observations to estimate evaporation in two northern hydropower reservoirs using different methods, with the bulk transfer equation showing the highest accuracy. Accuracy in estimating open water evaporation requires representative measurements of wind speed and water surface temperature.
HYDROLOGICAL PROCESSES
(2021)
Article
Soil Science
Jean-Daniel Sylvain, Francois Anctil, Evelyne Thiffault
Summary: The study combines bias correction and ensemble modeling to improve the accuracy of digital soil mapping, reduce conditional bias, and provide uncertainty assessment. The performance of ensemble modeling surpasses individual models and underdispersion in uncertainty analysis is identified. Global mapping products show low performance and important conditional bias compared to the proposed approach.
Article
Engineering, Civil
Mohammed Amine Bessar, Francois Anctil, Pascal Matte
Summary: The reliability and accuracy of the hydrometeorological ensemble prediction system coupled with a hydraulic module were evaluated in this study, showing that the proposed system provides reliable ensemble flow and water level forecasts across different forecast horizons.
JOURNAL OF HYDROLOGY
(2021)
Article
Engineering, Civil
Flore Sergeant, Rene Therrien, Ludovic Oudin, Anne Jost, Francois Anctil
Summary: The study found that during the period from 1970 to 2000, there was a significant decrease in recession slope and initial recession outflow in most Arctic catchments, contrary to previous research. High topography and low permafrost extent were identified as controlling factors that complicated the relationship between recession parameters and active layer thickness evolution.
JOURNAL OF HYDROLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Georg Lackner, Daniel F. Nadeau, Florent Domine, Annie-Claude Parent, Gonzalo Leonardini, Aaron Boone, Francois Anctil, Vincent Fortin
Summary: This study examines the surface energy budget of a subarctic shrub tundra site in eastern Canada, finding that turbulent heat fluxes in this region are more complex compared to other Arctic sites, mainly influenced by the soil moisture properties.
JOURNAL OF HYDROMETEOROLOGY
(2021)
Article
Environmental Sciences
Marinela del Carmen Valencia Giraldo, Simon Ricard, Francois Anctil
Summary: There is ongoing debate about whether probabilistic (top-down) or possibilistic (bottom-up) approaches are more suitable for estimating potential future climate impacts. In the context of deep uncertainty, bottom-up approaches that assess the sensitivity and vulnerability of systems to climate changes have become more popular. This study proposes a refined framework that combines the scenario-neutral method of the bottom-up approach with elements of the top-down approach. The results reveal regional and differential behaviors of hydroclimatology and low flows under different climate scenarios.
Article
Water Resources
Adrien Pierre, Daniel F. Nadeau, Antoine Thiboult, Alain N. Rousseau, Alain Tremblay, Pierre-Erik Isabelle, Francois Anctil
Summary: Water bodies such as lakes and reservoirs influence the regional climate through the evaporation of water. This study analyzed in-situ observations of a reservoir in a subarctic environment to understand its impact. The results showed that the annual evaporation rate was 590 +/- 66 mm, accounting for approximately 51% of the annual precipitation. The study also revealed the opposite diurnal cycles of sensible and latent heat fluxes during the open water period.
HYDROLOGICAL PROCESSES
(2023)
Article
Engineering, Civil
Michael Osina Torres, Amaury Tilmant, Emixi Valdez Medina, Francois Anctil, Maria-Helena Ramos
Summary: Improving the operational effectiveness of hydropower systems is crucial due to the shift to renewable energy sources and increasing costs associated with new hydro facilities. This study focuses on the relationship between short-term streamflow forecasts and hydropower generation, as well as the impact of uncertainties on energy output. A numerical experiment using hydrologic ensemble forecasts and reservoir optimization models was conducted in Canada. The results show that forecast quality affects energy production, but it is not a one-to-one causal relationship. Additionally, the diversity of hydrological models contributes to energy production, suggesting the value of model structure diversity.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2023)
Article
Engineering, Civil
F. Sergeant, R. Therrien, F. Anctil, Laura Gatel
Summary: In cold regions, climate warming causes permafrost thaw and changes the groundwater flow dynamics from local to regional systems. The recession slope of arctic catchment hydrograph is linearly related to permafrost thawing depth, making recession analysis a valuable method to study permafrost thawing dynamics in areas with limited permafrost observations. However, the linear relationship is influenced by permafrost extent, landscape topography, and aquifer properties.
JOURNAL OF HYDROLOGY
(2023)
Article
Geosciences, Multidisciplinary
Pierre Valois, Francois Anctil, Genevieve Cloutier, Maxime Tessier, Naomie Herpin-Saunier
Summary: The frequency and severity of flooding events are expected to increase with climate change in Quebec. A longitudinal study conducted in the province examined the adaptive behaviors of residents in high flood risk zones, finding that there has been no significant increase in adaptive behavior between 2015 and 2019. However, households that have experienced a flood or flood alert in the past are more likely to adapt. The study also identified income, flood experience, and perception of living in a flood-prone zone as important predictors of behavior adoption rates.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Geosciences, Multidisciplinary
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, Francois Anctil
Summary: A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. The method combines asynchronous hydroclimatic modelling and quantile perturbation applied to streamflow observations. The results show that the proposed workflow produces useful and reliable hydrologic scenarios, which can predict seasonal mean flows similar to a conventional hydroclimatic modelling approach.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Jing Xu, Francois Anctil, Marie-Amelie Boucher
Summary: Forecast uncertainties are inevitable in deterministic analysis of dynamical systems. Ensemble forecasting is an effective tool to represent error growth and capture uncertainties. This study compares the performance of evolutionary multi-objective optimization with a conventional state-of-the-art post-processor in eliminating forecast biases and maintaining proper dispersion. The evolutionary multi-objective optimization method demonstrated superiority in communicating with end-users for performance improvement.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Geosciences, Multidisciplinary
Emixi Sthefany Valdez, Francois Anctil, Maria-Helena Ramos
Summary: This study investigates the interactions between a precipitation post-processor and other uncertainty quantification tools in a hydrometeorological forecasting chain. The results show that the post-processor significantly improves the quality of precipitation forecasts, but its effectiveness in improving hydrological forecasts depends on various factors such as the configuration of the forecasting system, forecast attribute, lead time, and catchment size. Therefore, the combined effect of the precipitation post-processor and other uncertainty quantification methods should be considered when designing or enhancing hydrometeorological ensemble forecasting systems.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Geography, Physical
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, Francois Anctil, Matthieu Lafaysse, Marie Dumont
Summary: Arctic landscapes are covered in snow for at least 6 months a year, and the energy balance of the snow cover plays a key role in influencing various factors. The study aimed to quantify major heat fluxes above, within, and below a low-Arctic snowpack. Results showed that radiative losses are counterbalanced by sensible heat flux, with minimal latent heat flux. The model reproduced the observed energy balance well, but had deficiencies in simulating turbulent heat fluxes at an hourly timescale due to atmospheric stratification effects.
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)